Python client for MLflow REST API
Python client for MLflow REST API.
- Unlike MLflow Tracking client all REST API methods are exposed to user.
- All class fields are validated with pydantic.
- Basic and Bearer auth is supported.
- All methods and classes are documented.
- There is no integration with ML frameworks and libraries. You should use official MLflow client instead.
- There is no integration with S3 or other artifact storage type. You should access it directly with boto3 or other client.
- Only Python 3.7+ is supported. Python 3.6 and lower already reached end of life.
Stable version is released on every tag to master branch. Please use stable releases on production environment. Version example: 2.0.0
pip install mlflow-rest-client==2.0.0 # exact version pip install mlflow-rest-client # latest release
Development version is released on every commit to dev branch. You can use them to test some new features before official release. Version example: 2.0.0.dev5
pip install mlflow-rest-client==2.0.0.dev5 # exact dev version pip install --pre mlflow-rest-client # latest dev version
git clone email@example.com:MobileTeleSystems/mlflow-rest-client.git cd mlflow-rest-client
Install dependencies for development:
pip install -r requirements-dev.txt
Install pre-commit hooks:
pre-commit install pre-commit autoupdate pre-commit install-hooks
Test pre-commit hooks run:
pre-commit run --all-files -v
Make sure you have an MLflow Tracking Server running.
from mlflow_rest_client import MLflowRESTClient client = MLflowRESTClient("https://mlflow.domain", ignore_ssl_check=True) experiment = client.get_or_create_experiment("experiment_name") run = client.create_run(experiment.id)
See sample.py for more examples.
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